Key Concept: Averages, Range, and Standard Deviation, with A level Biology Past-Paper Questions

You need to know some maths for A level Biology. This includes knowing how to interpret averages (mean, median and mode), ranges, and standard deviations to work out whether an experiment can be said to have shown an effect or not. Master this early on and it will not help you with exam questions, but also make it easier for you to learn the bits of the course that are explained using these statistical methods.

Why does Biology need so much data?

Maybe the guy at the back is just big for his age?

Researchers often want to compare two or more things. Which species of frog is heavier? Which type of soil grows taller plants? At what temperature do these bacteria divide fastest? At which pH are fish most active?

The biological world is complicated, so multiple, repeated measurements are usually required.

There are three main reasons for taking multiple measurements:

1. Measurement errors. It’s hard to take measurements in the real world. Even if you re-measure the exact same thing, and even if you use a well-calibrated tool, you might get a slightly different result each time. Maybe you can’t hold the tool still enough, or you can’t read it clearly, or the thing you’re measuring moves. These are precision errors.

2. Individual variation. If you want to ask a general question about a whole population, eg “do robins sing more than blackbirds” then you need to measure data from more than two individuals. If you only use two, you might randomly pick outliers; maybe you get a particularly perky robin, or a lazy/sick blackbird. Similarly, if you sample a small area of a larger region, you may not pick a representative area.

3. Uncontrolled variables.There will nearly always be variable-influencing factors that you’re not aware of, or unable to control. Maybe there are changing sounds or smells in the environment, subtle changes in light, or in the birds’ blood-sugar levels. These can affect individual measurements in unpredictable ways.

All of these things can affect the value you record, making any one single measurement unreliable. So researchers normally end up collecting large sets of measurements. In this way they can get a much better idea of what’s really going on.

Why does Biology need Statistical techniques?

Plotting lots of repeated measurements for different datasets on the same graph can create a confusing mess. Also, “the data look different to me” isn’t good enough for science.

Reducing each dataset to just two or three values makes it much easier to compare. In fact, it’s so simple that such data can be understood even without a graph, so values are often presented very simply in a table.

Calculating Averages in Biology

There are three types of average: mean, median, and mode. They all reduce the data set to one single number.

This is useful for comparisons. For example, if you let a frog jump ten times, measuring the length of every jump, you can calculate their average jump length. You can then compare that single number to the average jump length from another frog to find out which jumps further.

Calculating the Mean

The most important type of average for A level Biology is the mean. It’s also what most people are talking about when they say “average” in everyday life.

To find the mean, add up all the numbers, then divide by how many numbers there were. You end up with just one number.

Here’s an example dataset:

How to calculate the mean:
3 + 4 + 5 + 5 + 5 + 6 + 6 + 6 + 7 + 8 = 55, so the total is 55
There are ten numbers, so n = 10
The mean is the total divided by n, which is 55/10, which is 5.5

Example exam question 1:

What is the missing number?

Find answers to Question 1 at the bottom of this webpage

Calculating the Median (unaffected by outliers)

What if we had the same data as above, but one of the measurements was … strange.

Set 1

3

4

5

5

5

6

6

6

7

8

total = 55   /   n = 10   /   mean = 5.5

Sometimes, datasets include odd numbers. It’s not clear whether the 48 here was an error in measurement, or whether it’s genuine. If it is measuring individual organisms, maybe the outlier is a strange mutant? But either way, outliers like this can do very strange things to the mean value.

How useful is the mean for this dataset?

To avoid the problem of a small number of outliers moving the mean away from where it would otherwise be, you can opt to use a different average, the median.

The median is found by putting the numbers in order of size (as they already are here) and picking the middle one. If there are two middle ones, take the mean of those two.

Here there are ten numbers. The two numbers in the middle are 5 and 6. The mean of these is (5+6)/2 = 5.5

Set 1

3

4

5

5

5

6

6

6

7

48

total = 95   /   n = 10   /   mean = 9.5

By ignoring the strange outlying number(s), we get an average that is more useful than the mean would be.

Example Exam Question 2:
How similar are the mean and median values for this data? (Answers at the end of this blog post.)

(d) Complete the table above to show the median and mean diameters.

Find answers to Question 2 at the bottom of this webpage

Calculating the Mode (the most common value)

There’s one more type of average value you need to know. The mode is just the number that is most frequently found in your dataset. Of course this only makes sense if there are plenty of repeated numbers present in the dataset.

Set 1

3

4

5

5

5

6

6

6

7

48

central number(s) = 5 and 6   /   median = 5.5

The mode is another way to stop outliers affecting your average.

Choosing which type of average to use

You might be asked to choose which average is most appropriate. Can you answer this exam question?

Example Exam Question 3:

Find answers to Question 3 at the bottom of this webpage

Moving Beyond the Average

Why the average isn’t enough

There’s a big problem with just using the average by itself to compare two sets of data. The problem is that very, VERY different sets of data can give you the exact same average value.

Compare these three sets of data:

Set 1

1

3

5

5

5

5

6

6

7

48

mode = 5

Set 1		50	50	50	50	50	50	50	50	50	50		mean = 50   /   median = 50   /   mode = 50
Set 2		25	30	35	40	50	50	60	65	70	75		mean = 50   /   median = 50   /   mode = 50
Set 3		1	2	3	4	50	50	96	97	98	99		mean = 50   /   median = 50   /   mode = 50

The averages are the same! By themselves, averages only tell you one small part of the story.

What is Range / why is it useful

One of the big differences betwen the datasets above is the range of numbers that appear.

The range is the range-of-values that appear, from the lowest to the highest.

Set 1		50	50	50	50	50	50	50	50	50	50		lowest value = 50   /   highest value = 50   /   range = 50 to 50
Set 2		25	30	35	40	45	55	60	65	70	75		lowest value = 25   /   highest value = 75   /   range = 25 to 75
Set 3		1	2	3	4	50	50	96	97	98	99		lowest value = 1   /   highest value = 99   /   range = 1 to 99

Set 1 has a range of 50 to 50. So you can reasonably predict that the next measurement would likely be 50 too
Set 2 and Set 3 have wider ranges. There are a wider range of possible values that might be measured, so it’s harder to predict what the next measurement might be.

A wide range might indicate that your measurement technique is very unprecise, or that there is a wide natural variation in the thing you are measuring, or that there is another factor affecting your measurements.

But a wide range might also just mean there were one and two weird outliers in the data. So you need to be careful when using this value. Here is a set with one odd measurement, which might be due to a measurement error.

Finding the Range

Example Exam Question 4:

The answer is at the bottom of this webpage

Why do we need Standard Deviation

The Standard Deviation tells you how similar the numbers you used to calculate your mean are. Were they very close together in value, or very different?

It’s different from the range because it tells you how closely the measurements were clustered around the mean. This tells you how useful the mean will be when comparing it to the mean from other data sets. It is also not affected by weird outliers in the way that the range is.

These two data sets have the same mean averages (50) and the same range (25-75):

Set 4

50

50

50

50

50

50

50

50

50

90

lowest value = 50   /   highest value = 90   /   range = 50 to 90

Set 1		25	42	48	50	50	50	50	52	58	75		values clustered around mean = low standard deviation
Set 2		25	30	35	40	45	55	60	65	70	75		values spread out away from mean = high standard deviation

To understand Standard Deviation, think about a situation where you have made very many measurements, so that you have multiple measurements at each possible value. Now plot these on a graph (see below). In biology, you usually see that the graph forms a bell shape. This is called a “Normal distribution”.

Normal distributions are symmetrical, so the mean, mode, and median are all the same, appearing at the centre of the graph (mean, median, and mode = 16 in this example). In normal distributions, most measurements are near the average, so there is a peak in the middle of the graph.

(Sometimes, you’ll find a curve is skewed a bit to one side. This separates out the mode, median and mean values. But for our purposes, I’m going to stick to thinking about the symmetrical graph.)

How wide the curve is matters a lot, because it affects how much two sets of data overlap. Compare these two examples below. Both have one set of data where the mean is 14 (plotted in orange), and another set where the mean is 20 (plotted in blue).

There is the same amount of data in both graphs, and the averages haven’t changed. But there is a lot less overlap between the two datasets in the example to the left. The data on the right is a lot more spread out away from the average values.

When datasets overlap a lot, you need to be very careful that you definitely have enough data to be sure their means really are different. If you have a small data set with a lot of variation, then adding extra measurements can make a big difference to the mean.

What is Standard Deviation

Standard Deviation tells you how widely the data is spread out in a normal distribution. Its symbol is sigma, “σ”.

You’re very unlikely to be asked to calculate standard deviation in an exam, and it takes a while to explain so I’m not going to go through it here (don’t worry they’d give you the equation if you did have to do this).

But you do need to know what it tells you.

Here is the basic normal distribution graph again. The graph is symmetrical and the mean (μ) is in the centre.

Now here is the same graph, but two more values are marked on the x-axis, shown by orange lines. These are the value of the mean minus one standard deviation (μ-σ), and the value of the mean plus one standard deviation (μ+σ).

If you colour in the bit of the graph that is within one standard deviation of the mean (from μ-σ to μ+σ), then on any normal distribution, 68.27% of the data points will lie within this area. You don’t need to remember that percentage, but remember it is always the same.

This means that if the standard deviation is a small number, you know most of the data points are close to the mean. This gives you more confidence that the mean is a useful value for comparison.

The graphs below have the same X-axis. Both are normal distributions with the same mean. But the one on the left has a small standard deviation, and the one on the right has a high standard deviation. (Some of the data from the right-hand graph falls outside the values shown on the graph.)

How to tell if there is a significant difference between values using the mean and standard deviation

!! Ok so this is the important bit we’ve been building up to !!

In a normal distribution, most of the data (68.27%) falls within one standard deviation of the mean. This is the area between μ-σ and μ+σ.

To work out whether it’s just chance that the means are different, or whether it’s a real effect, you need to check whether this area overlaps bewteen the two sets of data.

If the areas between μ-σ and μ+σ overlap, the difference is not considered significant.

There are different ways of presenting the data.

Standard Deviations Using Numbers - example

An example:

Set 1: mean (μ) = 50, standard deviation (σ) = 8
Set 2: mean (μ) = 40, standard deviation (σ) = 3

Are these sets of data significantly different? Look at the areas between μ-σ and μ+σ

Set 1: μ-σ = 42 and μ+σ = 58
Set 2: μ-σ = 37 and μ+σ = 43

Do these areas overlap? Yes they do (both include 42-43). So you can not consider the two data sets significantly different.

(Also worth knowing: nearly all the data (95.45%) falls within two standard deviations (between μ-2σ and μ+2σ) - so if these two areas don’t overlap you can be even more sure the two sets of data really are different.)

Standard Deviations Plotted on Graphs - example

On graphs, the mean is plotted as usual, with a dot or column. Extra lines extend out to show the area from μ-σ to μ+σ. This can make it more obvious whether areas overlap or not (unless they are super close in which case numbers are more useful).

Standard Deviation Exam Past Papers

Example Exam Question 5

Question 5 answers found at the bottom of this web page

Understanding Standard Deviations from Graphs

Example Exam Question 6

Question 6 answers found at the bottom of this web page

Example Exam Question 7

Question 7 answers found at the bottom of this web page

Graphs and Tables in A level Biology

If you’re not confident with questions that include graphs and tables, see the recent blog post “How to Approach A level Biology Graph and Table Questions: Tips and Exam Question Pack”, which offers more useful tips for navigating them during exams, and more exam questions to practice with.

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Answers to example exam questions

1. The data for the damaged block should be ignored. The mean for shape C is 3520 seconds

2. Cinnamon Oil median = 16, mean = 17 ….. and ….. Postive Control median = 12, median = 13

3. Median = 41. This avoids the outliers affecting the value as would happen if you used the mean. And the sample size is too small to use the mode (there are no repeated values)

4. The range is 2 to 11

5. Bull terrier genetic diversity is significantly the smallest of the breeds shown, meaning it the most inbred. Jack Russell genetic diversity is significantly the greatest. The genetic diversity of Miniature terrier and Airedale terriers are similar with no significant difference between the two.

6. Standard deviation is spread of data around the mean; using standard deviation reduces effect of anomalies/ outliers; standard deviationcan be used to determine if (the difference in results is) significant/not significant/due to chance /not due to chance

7. Trapping increases enzyme/GOx/HRP activity; the difference/increase is significant (it is unlikely to be due to chance as the standard deviations do not overlap)

How to approach and answer AQA A-level Biology Questions that need you to interpret Graphs- lots of AQA past paper questions

Magical top tip: DO NOT LOOK at the question first - LOOK at the data first

This simple trick can transform how easily you will be able to answer graph questions

• ALWAYS LOOK AT THE DATA FIRST

• Look closely at the graph or table

• Graphs - look very carefully at the axes - have they plotted rate or time, mass/volume or concentration ? Often students assume enzyme graphs have rate on the y axis - sometimes they don’t !

• Table - is the IV in the first column ? What are the units of the DV ? Has a mean been calculated ? Is the data in each row consistent ?

• are there range/SD bars on the graph ? remember this indicates the variation in the data that was used to calculate the mean

• do the range bars or standard deviation bars overlap ?,

• If the Standard deviations (+or- 2 SD overlap then the DIFFERENCE between the MEANS is due to chance - the differnce between the means is not statistically significant).

• In a table what range is in the replicates when you compare to the mean ?

• what trends can you observe ?

• then think about what principle of biology is being shown by the the trends, for instance - enzymes and substrate concentration or mitosis and distance from the root tip

• How would you explain the highest value, the lowest value, the point at which the line crosses the x axis, how would you explain the largest range, how would you change the method to reduce the spread in the data ?

Have a look at these 20 excellent recent graph interpretation AQA Questions

An old collection of OCR maths-heavy questions for extra practice

Y12 and Y13 AQA small group weekly class information

Key Concept: Independent and Dependent Variables

A guest blog from Dr Jenny Shipway, who studied biochemistry at university and now works in science communication and education training.

To understand experimental design and graphs in exam questions, you will need to confidently recognise the difference between different types of variables. Master this now and it will also make it easier to learn content that is taught using graphs.

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What is a Variable

A variable is any value that does/might change during an experiment.

Variables can include things like pH, temperature, colour, or the concentration of substances. The amount of time that has passed is a variable, as are rates of reaction.

• Some variables’ values are fixed deliberately by the scientist.

• Some variables’ values are changed deliberately by the scientist. These changes are planned in advance so the measured values are usually known before the experiment starts.

• Other variables’ values are allowed to change naturally as the experiment progresses. These values can be measured to provide useful data.

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Simplify Your Exam Approach

During exams it’s very easy to get overwhelmed with information. When this happens, your brain makes guesses and jumps, often without you being aware. This is why students so often mis-read graphs and/or make ‘silly’ mistakes.

Excellent Exam Tip: work out what the experiment was and how the graph is presented BEFORE looking at the exam question.

This will …

• Reduce the number of things you’re thinking about at one time

• Reduce the risk of you jumping to (incorrect) conclusions

• Help you focus on understanding the experiment

You might feel an urge to rush forward to look at the question, but trust me that won’t save time. You will not be able to answer the questions unless you understand the graph, and rushing forward will make that more difficult, not less.

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Identifying Variables

Variables described in the experiment and named on the graph axes may include:

• Independent Variable – this is the thing that is changed deliberately by the scientist in a planned way. This is the thing that we expect to cause a measurable effect. 

• Dependent Variable- this is the thing that is measured by the scientist. The value is not known until it is measured, and the value will depend upon the value of the independent variable.

• Control Variables – these variables are fixed to one unchanging value throughout. For example, all experiments might be carried out at 20°C, in which case temperature is a control variable. (Important: this is a different thing from ‘a control’).

The purpose of the experiment is to answer the question: how does the independent variable affect the dependent variable?

There are usually many factors that could be affecting the dependent variable, so it’s important to control as many variables as possile. The idea is that if only one thing has changed (ie the independent variable), then you can be more confident the affect is due to that one thing. 

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An Example 🍅🍅🍅

If you wanted to work out what temperature was best for growing tomatoes, you could try growing plants at different temperatures to see which plant produced the most fruit. Everything except temperature should be kept the same so that you’re sure that any difference in fruit yield is due to temperature.

The question is: how does temperature affect fruit yield?

You will deliberately be changing the temperature to pre-chosen values, so temperature is the independent variable.

You will need to measure the weight of tomatoes produced to find out this value. So this is the dependent variable. The weight of fruit will depend upon the temperature.

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Check your Understanding

Can you identify the independent and dependent variables in the following examples? Which variable was independently fixed by the scientist? And which was measured/recorded during the experiment?

(Ignore the positions of the data points, you only need to look at the axis labels.)

Usually, you will find the independent variable along the x-axis, and the dependent variable up the y-axis. But that’s not always the case (as you hopefully spotted in some of these examples). Do not let your brain jump to an assumption! You must always check this.

You need to be 100% confident of your variables before you move forward to look at the actual question, or everything else will get really confusing. It’s a good tip to write on the exam paper which variable is which (eg label the graph axes “I.V.” and “D.V.”)

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Multiple Independent Variables

It’s possible to have more than one independent variable.

For example, some experiments are run twice under different conditions. See this graph:

First, look at the axes. The independent variable here is the amount of time that has passed (on the x-axis): the scientist decided before the experiment at what times they would count the fruit. The dependent variable is the number of ripe fruit (on the y-axis): the number of ripe fruit is the thing the scientist is measuring, and this depends on how much time has passed when the measurement is taken. We’ve seen this before.

Now look at the data. There are two sets of data plotted on the graph. One experiment has been run with fertiliser, and one without. So the presence of fertiliser is another independent variable – it’s something else that affects the value of the dependent variable.

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Example A-level Exam Questions

Can you identify the independent and dependent variables in the following A level Biology exam questions?

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If this post has been helpful, please like ❤️ below and share with your friends. 

For more general information about graphs, see the post about how best to approach A level biology graph questions.

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How to Approach A level Biology Graph and Table Questions: Tips and Exam Question Pack

Get top marks when analysing figures, tables and images by avoiding common mistakes that students make

This article contains key vocabulary, a strategy for how to approach questions for success, a multichoice quiz with answers, and a big pack of past paper exam questions

Don’t panic, it’s only a graph

The single best exam tip for graphs and tables exam questions is to start by looking at the graph or chart itself. DO NOT LOOK AT THE QUESTIONS FIRST! This single thing will help you avoid the most common mistakes that students make.

But you also need to know what you’re doing. Which means you’ll need to be confident with these terms:

Background Knowledge / Vocabulary:

• Independent Variable: The variable that you purposefully set to different values during the experiment

• Dependent Variable: The variable that you measure during the experiment, which is unknown until it is measured

• Replicate: Experimental data is often replicated - the same data point is recorded multiple times for the same conditions

• Accuracy / Precision: Accuracy is how close the replicated values are to the correct value, and precision is how close they are to each other. If there is an unknown problem with the experiment, results can be very precise but have very low accuracy.

• Range / Standard Deviation: The amount of variation in the data. A large range or standard deviation means that the replicated data had a broad range of results. A small range or standard deviation means they were much more similar in value. Range / Standard deviation is therefore a measure of precision.

• Trend: What is the general relationship between the dependent and independent variables? When the experimenter increased the independent variable, what happened to the dependent variable? What shape is the graph?
  

How to Approach the Question:

1. Look at the graph or chart first!
Too many students look at the question first, get confused or panicky about what it is asking, and form preconceptions about what data they need. This then means they are then unable to look at the data clearly, and miss the information they actually need. Looking at the graph or chart first both makes the data easier to understand, and makes it easier to work out what the question is asking.

Trust me, this is a major factor in student success. If you only take away one thing from this article, always look at the graph or chart first.

2. Don’t panic if it’s about something totally unfamiliar
Students can get very thrown if the question is about an organism or molecule that they have never heard of before (the exam boards do this a lot). This sudden panic makes it hard to think clearly.

Remember - if you have covered all the course material, even if the question is about something weird and new then all the information you need will be in the data. The things that look scary are just surface details. If the question was “Fred gave James two apples, how many apples does James have” you wouldn’t need to know who these people were to answer the question.

But don’t just dive in to the details of the data …

What’s going on here?

3. Understand the format
Don’t waste time looking at the actual dots or numbers until you understand how the data has been presented. Check every aspect methodically. It’s too easy to make assumptions based on previous graph/table formats you have seen - this one might be different!

• Look at the headings / axis labels and units. What is the data showing?

• Identify the independent variable and the dependent variable. If possible, it’s helpful to label them “IV” and “DV”.

• What type of data is shown? Is it averages? Does it include a Range or Standard Deviation?

• Graphs: Check the axis labels. Have they plotted rate or time, mass/volume or concentration? Often students assume enzyme graphs have rate on the y axis - but sometimes they don’t!

• Tables: Check: is the Independent Variable in the first column? Is the data in each row consistent?
  
  

4. Look at the data
Now you understand its context, look at the actual dots or lines or numbers. Check:

• Does the Range overwhelm differences in values: Do the range bars or standard deviation bars overlap? If they do, then there is significant overlap between the populations of replicated results that were used to calculated the average values.

• Unspecified Ranges: If there are replicates but no range bars or standard deviation has been calculated, how broad does the range look when you compare the replicated data values to their mean?

• Trends: What trends can you observe? Then think about what principle of biology is being shown by the the trends.

Now think about what it all actually means:

• Values: How would you explain the highest value, the lowest value, the point at which the line crosses the x axis,

• Range: How would you explain the largest range? How would you change the method to reduce the spread in the data?
  
  

5. Ok - NOW look at the actual questions
Try to see past the detail. How does this data/question relate to things you have studied?

Your working memory can easily get overloaded with details, making it hard to think. If the examiners have introduced a new organism, its name won’t be important. What might be important is the environment in which it lives, or its interactions with other organisms. You know what data you have, and what the questions are, so pick out what actually matters here. Is this a question about enyme reaction rates? Or about surface area to volume ratio?

This is why it’s useful to look at the data first - you will be able to look at it with a clear eye, making it easier to pick out how it’s relevant to the material you have studied.

6. Give the required information
Avoid the common mistakes that lose students marks:

• If they say you should use the data, you must either quote it, or show how you have used in in a calculation

• Refer to the axis/data labels wherever possible. Don’t say “the graph goes up”, do say “the saturation of haemoglobin increases”

A-Level Biology Past Paper Graphs and Charts Exam Questions:

Got all that? Ok! Here are some questions for you to practice.

And remember - don’t read the questions until after you have made sense of the graph or chart.

If you’re looking for more maths-heavy questions to practice, here is an older but popular set from OCR.

If this post has been helpful, please like ❤️ below and share with your friends. 

This article was written by Dr Jenny Shipway with guidance and editing from Tom. Tom has over 26 years experience specialising in A level Biology teaching and tuition, and has helped many students achieve top grades in the subject.

Calculations - Mathematical Content in A level Biology ..... Some easy some not so easy from AQA Biology

10% of the marks in Biology papers are for calculations. Here are some good practise questions and a great advice document from OCR (applicable to all boards)

Answering Questions with lots of Maths in Biology - Data Analysis Questions in the new OCR Biology A Specification - updated Feb 2018 with the latest handbook

The quantity of maths in the 2017 specification is a challenge to many students - so I put together a summary of all of the OCR A new spec questions with some element of data analysis.

42 pages with markschemes - have a look at the pdf

Also have a look at this guidance  on tables and graphs

And the mathematical skills for OCR guidebook

Please like and share (and click on a advert to help with the hosting costs !)

OCR A - Guides to the A level Biology Exam Papers from alevelbiologytutor on Vimeo.

A-level Biology past paper questions with Graphs

Lot of people requested more analysis questions with graphs - so here are plenty more with the markschemes.

Click on the graph to access the full resource.

Please like and share (and click on a advert to help with the hosting costs !)

How to approach and answer A-level Biology Questions that need you to Analyse Figures, Tables and Images - lots of example past paper questions

DO NOT LOOK at the question and then look at the data to answer the question.

• ALWAYS LOOK AT THE DATA FIRST

• Look closely at the graph or table

• Graph look very carefully at the axes - have they plotted rate or time, mass/volume or concentration ? Often students assume enzyme graphs have rate on the y axis - sometimes they don’t !

• Table - is the IV in the first column ? What are the units of the DV ? Has a mean been calculated ? Is the data in each row consistent ?

• are there range/SD bars on the graph ? remember this indicates the variation in the data that was used to calculate the mean

• do the range bars or standard deviation bars overlap ?

• In a table what range is in the replicates when you compare to the mean ?

• what trends can you observe ? then think about what principle of biology is being shown by the the trends.

• How would you explain the highest value, the lowest value, the point at which the line crosses the x axis, how would you explain the largest range, how would you change the method to reduce the spread in the data ?

Once  you have a coherent understanding of the trends  - only then look at the question.

try this - about 20 questions with data and analysis

If you found it useful then please ❤️ (at the bottom of the page) and share, you can follow me on instagram - alevelbiologytutor

Tuesday night group tutoring topics and Y13 & Y12 OCR A and AQA small group weekly class information

How to do Tables and Graphs - terrific guidance from Exam Boards for A-level Biology

Rather than being assessed seperately, practical skills are included in the theory paper. Like this OCR question from the sample paper

Please like and share (and click on a advert to help with the hosting costs !)

There is plenty of excellent guidance on how to plot graphs and draw tables in the OCR practical booklet  ..... pdf here

Precise but not accurate ? Language of Measurement - crucial for Biology Practical Exam Questions

Precision, control variables, independent variables ? Confusing for many students, however, particularly important now that this year practical skills are assessed in the exam paper. 

An extremely useful document is the Language of Measurement. Reading it is a great start to improving your approach to practical questions. Below are 3 pages.

Original PDF - is here